#multitasklearning #biology #neuralnetworks
Catastrophic forgetting is a big problem in mutli-task and continual learning. Gradients of different objectives tend to conflict, and new tasks tend to override past knowledge. In biological neural networks, each neuron carries a complex network of dendrites that mitigate such forgetting by recognizing the context of an input signal. This paper introduces Active Dendrites, which carries over the principle of context-sensitive gating by dendrites into the deep learning world. Various experiments show the benefit in combatting catastrophic forgetting, while preserving sparsity and limited parameter counts.
OUTLINE:
0:00 – Introduction
1:20 – Paper Overview
3:15 – Catastrophic forgetting in continuous and multi-task learning
9:30 – Dendrites in biological neurons
16:55 – Sparse representations in biology
18:35 – Active dendrites in deep learning
34:15 – Experiments on multi-task learning
39:00 – Experiments in continual learning and adaptive prototyping
49:20 – Analyzing the inner workings of the algorithm
53:30 – Is this the same as just training a larger network?
59:15 – How does this relate to attention mechanisms?
1:02:55 – Final thoughts and comments
Paper:
Blog:
ERRATA:
– I was made aware of this by “That axon you showed of the pyramidal neuron, is actually the apical dendrite of the neuron”. Sorry, my bad 🙂
Abstract:
A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows. First, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model’s prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results on both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.
Authors: Abhiram Iyer, Karan Grewal, Akash Velu, Lucas Oliveira Souza, Jeremy Forest, Subutai Ahmad
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